摘要
针对多类分类问题,研究支持向量机集成中的分类器组合架构与方法.分析已有的多类级和两类级支持向量机集成架构的不足后,提出两层的集成架构.在此基础上,研究基于证据理论的支持向量机度量层输出信息融合方法,针对一对多与一对一两种多类扩展策略,分别定义基本概率分配函数,并根据证据冲突程度采用不同的证据组合规则.在一对多策略下,采用经典的Dempster规则;在一对一策略下则提出一条新的规则,以组合冲突严重的证据.实验表明,两层架构优于多类级架构,证据理论方法能有效地利用两类支持向量机的度量层输出信息,取得了满意的结果.
Ensemble learning has become a main research topic in the field of machine learning recently. By training and combining some accurate and diverse classifiers, ensemble learning provides a novel approach for improving the generalization performance of classification systems. Studied in this paper are the architectures and methods for combination of multiple classifiers in support vector machine （SVM） ensemble for multi-class classification. After analyzing the defects of the known architectures including multi-class-level SVM ensemble and binary-class-level SVM ensemble, a two-layer architecture is proposed to construct SVM ensemble. Then fusion methods of the measurement-level output information of SVMs are studied based on the evidence theory. Different basic probability assignment functions are defined respectively in terms of the used strategy for multi-class extension, i.e. one-against-all and one-against-one, and different evidence combination rules are adopted according to the degree of conflicts among evidence. In the case of one-against-all strategy, the classical Dempster＇s rule can be used while in the case of one- against-one strategy a new rule is proposed to combine the heavily conflicting evidence. The experimental results show that the two-layer architecture is better than the multi-class-level architecture. Moreover, the evidence theory based methods can effectively utilize the measurement-level output information of binary SVMs so as to gain satisfactory classification accuracies.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2008年第4期571-578,共8页
Journal of Computer Research and Development
基金
国家“九七三”重点基础研究发展规划基金项目(2002cb312200)
国家“八六三”高技术研究发展计划基金项目(2002AA412010)
国家自然科学基金项目(60575036)
关键词
支持向量机
集成
分类器组合
多类分类
证据理论
support vector machine
ensemble
combination of multiple classifiers
multi-class classification
evidence theory
